Wireless communication networks are well known and increasing in popularity. Mobile terminals, such as cell phones, wirelessly communicate through base stations or node B's in WCDMA that are associated with different cells or sectors in a geographic region, for example. With the increasing popularity and increased competition in wireless communications, system reliability and availability to the end user, is increasingly important.
The costs for operating radio networks are an increasing part of the operator's expenditure (OPEX). The OPEX is expected to further increase as the number of base stations, their geographical distribution across a wide area and the nature of wireless links require complex and sophisticated fault detection and recovery schemes. Fault detection is a required element of an approach to maintaining high reliability and availability. The challenge is to keep capital expenses under control while maintaining reliability and availability.
One significant scenario that can affect the reliability and availability of a wireless system is when one of the cells is a “sleeping cell”. A sleeping cell as that term is used in this description is a faulty cell that has not generated an alarm or in any other way indicated a problem but still is not capable of carrying traffic. This can be caused e.g. by faulty configuration, faulty radio circuits or other critical hardware, memory or other resource leakage. One particular example could be that the configuration of broadcast information is corrupt so that mobile stations cannot read the system information required to access the radio cell. The impact of a sleeping cell is that mobile terminals in the corresponding coverage area cannot be served as the service disappears in a sleeping cell.
Detecting a sleeping cell in the existing technology is performed by manually monitoring the traffic patterns in a network. Cells that normally carry non-zero traffic volumes but where the traffic volume is seen to be zero for some time are listed as candidates and the network operator then visits the cells and actively checks if the site is working or not. The main problem with this approach is that all cells during times have no traffic due to low subscriber activity. Hence it is difficult to deduce if a time period of no traffic in a cell is caused by the cell being a sleeping cell or caused by the fact that no subscriber has tried to access the system. This causes problems in particular in cells with only moderate amount of daily traffic where a full day of zero traffic could but need not indicate a problem. Detecting a sleeping cell is more complex still when one considers that the amount of traffic varies at different times of the day during different seasons of the year.
A critical component in the method above is thus to determine just how long time of no traffic indicated should qualify a cell as a candidate sleeping cell. A too long time, like a week, would mean that truly sleeping cells would be identified and fixed at the earliest one week after the problem of the sleeping cell first occurred. Thus to set a long time before to react can lead to lengthy periods of service absence.
In contrast, a too short time, like an hour, would lead to many perfectly well-working cells (which due to subscriber inactivity have served no end-user during the hour) would be put on the list for time-consuming site visits. Hence a too short time leads to unnecessary and costly site visits to well-working cell. This leads to unnecessary and costly site visits to cells identified as faulty but where the main reason of no traffic is that no user tried to be connected for one day.
Traditionally, operator takes action only when zero-traffic persists for quite some time, i.e. one to three days. This to avoid site visits due to false alarms. However cells that take traffic only few days per week, but which are still important due to service coverage consideration are virtually impossible to detect as sleeping cells.
Consequently, one serious drawback with the method described above is a trade off between costly site visits and the time to react.
Hence there exist a need for a method and arrangements for automatically detecting sleeping cells and that at the same time are efficient in terms of fast detection and economical in terms of less site visits due to false alarms.